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遥感 |免费全文 |一种使用无人机图像在作物行之间和作物行内绘制早期杂草的自动随机森林-OBIA算法
Source: mdpi.com
Article summary:
1. UAV images have been proven to be effective for early weed detection in crops.
2. OBIA technology combines digital surface models, orthomosaics and machine learning techniques (random forests, RF) to accurately estimate crop heights and classify images without manual training.
3. Weed maps can be created based on the OBIA algorithm to help farmers optimize crop management decisions by applying herbicides reasonably.
Article analysis:
The article is generally reliable and trustworthy as it provides a detailed description of the research conducted by the authors, including the methodology used, results obtained and conclusions drawn from them. The article also includes references to other relevant studies that support its claims.
However, there are some potential biases in the article that should be noted. For example, the authors do not discuss any possible risks associated with using UAVs for early weed detection or any potential negative impacts of using herbicides on crops or the environment. Additionally, while the authors mention that their method could be used for other crops besides grasses, they do not provide any evidence or examples of this being done successfully in practice. Furthermore, while they mention that their method could be used for different types of weeds, they only focus on one type of weed (grass). This may lead readers to believe that their method is only applicable to grasses when it may actually have wider applications than what is discussed in the article.
In conclusion, while this article is generally reliable and trustworthy due to its detailed description of research conducted and references to other relevant studies, there are some potential biases which should be noted such as lack of discussion about possible risks associated with UAVs or herbicides and lack of evidence for wider applications beyond grasses.